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Microbial Ecology

, Volume 68, Issue 1, pp 1–12 | Cite as

Pseudomonas aeruginosa Biofilms in Disease

  • Lawrence R. Mulcahy
  • Vincent M. Isabella
  • Kim LewisEmail author
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Abstract

Pseudomonas aeruginosa is a ubiquitous organism that is the focus of intense research because of its prominent role in disease. Due to its relatively large genome and flexible metabolic capabilities, this organism exploits numerous environmental niches. It is an opportunistic pathogen that sets upon the human host when the normal immune defenses are disabled. Its deadliness is most apparent in cystic fibrosis patients, but it also is a major problem in burn wounds, chronic wounds, chronic obstructive pulmonary disorder, surface growth on implanted biomaterials, and within hospital surface and water supplies, where it poses a host of threats to vulnerable patients (Peleg and Hooper, N Engl J Med 362:1804–1813, 2010; Breathnach et al., J Hosp Infect 82:19–24, 2012). Once established in the patient, P. aeruginosa can be especially difficult to treat. The genome encodes a host of resistance genes, including multidrug efflux pumps (Poole, J Mol Microbiol Biotechnol 3:255–264, 2001) and enzymes conferring resistance to beta-lactam and aminoglycoside antibotics (Vahdani et al., Annal Burns Fire Disast 25:78–81, 2012), making therapy against this gram-negative pathogen particularly challenging due to the lack of novel antimicrobial therapeutics (Lewis, Nature 485: 439–440, 2012). This challenge is compounded by the ability of P. aeruginosa to grow in a biofilm, which may enhance its ability to cause infections by protecting bacteria from host defenses and chemotherapy. Here, we review recent studies of P. aeruginosa biofilms with a focus on how this unique mode of growth contributes to its ability to cause recalcitrant infections.

Keywords

Cystic Fibrosis Cystic Fibrosis Transmembrane Conductance Regulator Cystic Fibrosis Patient Rhamnolipids Cystic Fibrosis Lung 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported by National Institutes of Health grant T-R01AI085585-01 and by Army Research Office grants W9911NF-09-1-0265 and 55631-LS-RIP.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Lawrence R. Mulcahy
    • 1
    • 2
  • Vincent M. Isabella
    • 1
  • Kim Lewis
    • 1
    Email author
  1. 1.Antimicrobial Discovery Center, Department of BiologyNortheastern UniversityBostonUSA
  2. 2.IonOptix, LLCMiltonUSA

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